Articles on data analysis and statistics in MQL5

The article presents CPdfBuffer and CPdfWriter, a minimal MQL5 pair that derives all offsets from the output buffer instead of hand-maintaining numbers. CPdfWriter records object starts, writes a fixed-width cross-reference table and trailer, and ensures structural consistency. The result passes Part 2's validator, and a ready-to-use script converts arbitrary text to a paginated PDF.

A published daily history of option-implied variance is combined with a broker symbol's daily bars to fit a HAR model of realized volatility in MQL5; the implied term enters the regression as a predictor, not as a ready-made forecast. Out-of-sample tests on four funds paired with their Cboe indices, ten US equities, and two broker symbols show where it improves the forecast. A background service refits on the broker's bars and draws a five-session price range on the chart, and a self-test matches independent C++ and Python implementations.

This article implements a multi‑timeframe Granger causality monitor for MetaTrader 5. A Jupyter notebook tests predefined timeframe pairs, selects lags, applies multiple‑testing control, and exports a calibration table. An MQL5 Expert Advisor reloads the setup, recomputes the F‑test live on rolling windows, and marks pairs VALID/INVALID to gate its vote‑based BUY/SELL generator. The workflow shows alignment rules, live/offline parity, and a filtered vs unfiltered comparison on EURUSD.

We build a neuron-level view for MQL5 models: count neurons per row, compute ranks, and render one line per stored weight from dense layers. The implementation reads matrix orientation from attributes, colors connections by sign and relative strength, and keeps activations on the same row. You can toggle between the layer graph and neurons, or see why the neuron view is unavailable, to analyze how weights connect inputs to outputs.

We estimate the probability, at broker time 08:00, that EURUSD's 08:00–16:00 range exceeds 1.25 times the mean of the previous 20 complete, valid target ranges. A two-feature SPSS logistic regression model employs the completed 00:00–08:00 H1 window. Coefficients from 2020–2023 sessions are fixed for the 2024 holdout comparison against constant and 60-outcome rolling frequency baselines, using Brier scores and log losses. MQL5 scripts allow readers to extract broker data and recreate the probabilities in MetaTrader 5.

CTradePreflight checks a trade request before OrderSend(), so an EA finds an off-step volume, a wrong-side stop, missing margin or a closed session instead of getting a server rejection. The class repairs only safe cases, reads the OrderCheck() retcode, and counts every refusal by reason. Unit tests and a random-delay stress EA show that levels placed very close to the price can still fail, and an optional delay buffer stops those rejections at the cost of blocking some valid requests.

A reusable MQL5 component measures floating P/L with swap, gross loss, two stop‑loss quantities, equity gap, margin level, and stop coverage, then classifies results for selected positions and the whole account. The snapshot records validity and readable reasons, supporting a quote‑age policy and missing‑stop handling. Integrate it into an Expert Advisor and block new exposure when thresholds or data checks fail.

A native MQL5 pipeline computes L-moments from probability‑weighted moments and compares their shape ratios to conventional Pearson estimators on the same rolling window. A single-pass walk-forward validator flags L‑kurtosis (tau4) and excess kurtosis spikes against adaptive baselines and scores them with precision, recall and F1 on real FX history. The script is diagnostic, input-driven, and avoids the Strategy Tester to provide reproducible, data-first results.